class_docstr: LSTM
  
  LSTM encoder, with MLP decoder.
  The network has `tanh` or `relu` non-linearities, it is trained using
  ADAM stochastic gradient descent. The network accepts static, historic
  and future exogenous data.
  
  **Parameters:**<br>
  `h`: int, forecast horizon.<br>
  `input_size`: int, maximum sequence length for truncated train backpropagation. Default -1 uses all history.<br>
  `inference_input_size`: int, maximum sequence length for truncated inference. Default -1 uses all history.<br>
  `encoder_n_layers`: int=2, number of layers for the LSTM.<br>
  `encoder_hidden_size`: int=200, units for the LSTM's hidden state size.<br>
  `encoder_activation`: str=`tanh`, type of LSTM activation from `tanh` or `relu`.<br>
  `encoder_bias`: bool=True, whether or not to use biases b_ih, b_hh within LSTM units.<br>
  `encoder_dropout`: float=0., dropout regularization applied to LSTM outputs.<br>
  `context_size`: int=10, size of context vector for each timestamp on the forecasting window.<br>
  `decoder_hidden_size`: int=200, size of hidden layer for the MLP decoder.<br>
  `decoder_layers`: int=2, number of layers for the MLP decoder.<br>
  `futr_exog_list`: str list, future exogenous columns.<br>
  `hist_exog_list`: str list, historic exogenous columns.<br>
  `stat_exog_list`: str list, static exogenous columns.<br>
  `loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>
  `valid_loss`: PyTorch module=`loss`, instantiated valid loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>
  `max_steps`: int=1000, maximum number of training steps.<br>
  `learning_rate`: float=1e-3, Learning rate between (0, 1).<br>
  `num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.<br>
  `early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.<br>
  `val_check_steps`: int=100, Number of training steps between every validation loss check.<br>
  `batch_size`: int=32, number of differentseries in each batch.<br>
  `valid_batch_size`: int=None, number of different series in each validation and test batch.<br>
  `scaler_type`: str='robust', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>
  `random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br>
  `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>
  `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>
  `alias`: str, optional,  Custom name of the model.<br>
  `**trainer_kwargs`: int,  keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>
code: def forward(self, windows_batch):
      # Parse windows_batch
      encoder_input = windows_batch["insample_y"]  # [B, seq_len, 1]
      futr_exog = windows_batch["futr_exog"]
      hist_exog = windows_batch["hist_exog"]
      stat_exog = windows_batch["stat_exog"]
  
      # Concatenate y, historic and static inputs
      # [B, C, seq_len, 1] -> [B, seq_len, C]
      # Contatenate [ Y_t, | X_{t-L},..., X_{t} | S ]
      batch_size, seq_len = encoder_input.shape[:2]
      if self.hist_exog_size > 0:
          hist_exog = hist_exog.permute(0, 2, 1, 3).squeeze(
              -1
          )  # [B, X, seq_len, 1] -> [B, seq_len, X]
          encoder_input = torch.cat((encoder_input, hist_exog), dim=2)
  
      if self.stat_exog_size > 0:
          stat_exog = stat_exog.unsqueeze(1).repeat(
              1, seq_len, 1
          )  # [B, S] -> [B, seq_len, S]
          encoder_input = torch.cat((encoder_input, stat_exog), dim=2)
  
      # RNN forward
      hidden_state, _ = self.hist_encoder(
          encoder_input
      )  # [B, seq_len, rnn_hidden_state]
  
      if self.futr_exog_size > 0:
          futr_exog = futr_exog.permute(0, 2, 3, 1)[
              :, :, 1:, :
          ]  # [B, F, seq_len, 1+H] -> [B, seq_len, H, F]
          hidden_state = torch.cat(
              (
                  hidden_state,
                  futr_exog.reshape(batch_size, seq_len, -1),
              ),
              dim=2,
          )
  
      # Context adapter
      context = self.context_adapter(hidden_state)
      context = context.reshape(
          batch_size, seq_len, self.h, self.context_size
      )
  
      # Residual connection with futr_exog
      if self.futr_exog_size > 0:
          context = torch.cat((context, futr_exog), dim=-1)
  
      # Final forecast
      output = self.mlp_decoder(context)
      output = self.loss.domain_map(output)
  
      return output
  
file_path: neuralforecast/neuralforecast/models/lstm.py
imports: from typing import Optional
  import torch
  import torch.nn
  from ..losses.pytorch import MAE
  from ..common._base_recurrent import BaseRecurrent
  from ..common._modules import MLP
label_desc: Use image node_id 2 for calling the LSTM obj's underlying member method code with example usage: obj.forward(windows_batch) and returns: output